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WORKSHOP: Polling the Profession - How Should We Teach With AI

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Presenting Author(s): Dr. Nia Abdullayeva

Date and time: 20 Mar 2026 from 14:30 to 15:15

Location: Mount Temple Ballroom C  Floor Map

Learning Objectives

1. Identify appropriate, ethical, and educationally sound uses of artificial intelligence in clinical psychiatry teaching, including its role in documentation support, case formulation scaffolding, and resident assessment preparation;

2. Critically evaluate the benefits, limitations, and potential risks of Al-assisted learning through structured discussion prompts and polling questions, with a focus on maintaining clinical judgment, reflective practice, and professional standards; and

3. Apply practical strategies for integrating Al into supervision and teaching encounters, including setting boundaries for learners, modeling responsible Al use, and incorporating Al literacy into training while preserving the humanistic core of psychiatric education.

Abstract

As artificial intelligence rapidly enters clinical and academic spaces, psychiatrists are increasingly expected to guide trainees in navigating Al tools safely, ethically, and effectively. This 45-60-minute workshop equips psychiatric educators with practical strategies for incorporating Al into clinical teaching while preserving the humanity, nuance, and relational depth central to psychiatric training. Using a highly interactive format, participants will engage in real-time polling and facilitated discussion to explore emerging questions at the intersection of Al, supervision, and professional identity

Through a series of provocative poll questions, attendees will examine topics such as the acceptability of Al-assisted documentation, the role of Al in preparing for examinations, the boundaries of its use in case formulation and psychotherapy teaching, and the extent to which Al should be explicitly taught as a core competency. Following each poll, structured discussion prompts will guide participants to analyze benefits, risks, and ethical considerations surrounding Al-mediated learning. These dialogues will highlight diverse perspectives on how Al may enhance efficiency, scaffold residents' learning, or risk diminishing critical reasoning and reflective practice

Participants will also explore how Al may shift the future landscape of psychiatric education, debating questions about supervision, simulated patient encounters, and the evolving responsibilities of faculty as role models in Al literacy. By the end of the workshop, attendees will have a deeper understanding of how to integrate Al thoughtfully into teaching encounters, set appropriate boundaries for learners, and maintain educational standards in an era of rapid technological change. This workshop aims to empower psychiatrists to lead with clarity, flexibility, and intentionality as Al becomes an integral component of medical education

Literature References

1. Benjamens, S., Dhunnoo, P., & Mesko, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. npj Digital Medicine, 3(1), 118https://doi.org/10.1038/s41746-020-00324-0 

2. Masters, K. (2019). Artificial intelligence in medical education. Medical Teacher, 41(9), 976-980. https://doi.org/10.1080/0142159X.2019.1595557 

3. Rudolph, J. W., Raemer, D. B., & Simon, R. (2014). Establishing a safe container for learning in simulation: The role of the presimulation briefing. Simulation in Healthcare, 9(6), 339-349https://doi.org/10.1097/SIH.0000000000000047 

4. Hasegawa, R. B., Phillips, M., & O'Leary, K. J. (2023). Large language models in medical education: Opportunities, challenges, and future directions. Academic Medicine. Advance online publicationhttps://doi.org/10.1097/ACM.0000000000005261 

5. Wong, B. M., Etchells, E., Kuper, A., Levinson, W., & Shojania, K. G. (2010). Teaching quality improvement and patient safety: A framework for integration into residency training. BMJ Quality & Safety, 19(1), 1–7. https://doi.org/10.1136/qshc.2009.037473 



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